A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans

Optical coherence tomography (OCT) is a noninvasive imaging modality that can be used to obtain depth images of the retina. Patients with multiple sclerosis (MS) have thinning retinal nerve fiber and ganglion cell layers, and approximately 5% of MS patients will develop microcystic macular edema (MME) within the retina. Segmentation of both the retinal layers and MME can provide important information to help monitor MS progression. Graph-based segmentation with machine learning preprocessing is the leading method for retinal layer segmentation, providing accurate surface delineations with the correct topological ordering. However, graph methods are time-consuming and they do not optimally incorporate joint MME segmentation. This paper presents a deep network that extracts continuous, smooth, and topology-guaranteed surfaces and MMEs. The network learns shape priors automatically during training rather than being hard-coded as in graph methods. In this new approach, retinal surfaces and MMEs are segmented together with two cascaded deep networks in a single feed forward propagation. The proposed framework obtains retinal surfaces (separating the layers) with sub-pixel surface accuracy comparable to the best existing graph methods and MMEs with better accuracy than the state-of-the-art method. The full segmentation operation takes only ten seconds for a 3D volume.

Section: Methodsmentioning

confidence: 99%

Section: Mme Evaluationmentioning

confidence: 99%

Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT

He¹,

Carass²,

Liu³

et al. 2019

“…Although relatively poor results were reported in a private dataset containing Cirrus SD-OCT volumes, the method achieved comparable results in the DME dataset used by a previously discussed method [37]. Finally, in a recent benchmark study, several methods for IRC segmentation were compared and evaluated in a publicly available dataset [46]. Unfortunately, only data from two of the four SD-OCT devices present in the dataset were included for analysis.…”

Section: Introductionmentioning

confidence: 98%

Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography

Venhuizen

Ginneken

Liefers

et al. 2018

126

We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition. A cascade of neural networks was introduced to include prior information on the retinal anatomy, boosting performance significantly. The proposed algorithm approached human performance reaching an overall Dice coefficient of 0.754 ± 0.136 and an intraclass correlation coefficient of 0.936, for the task of IRC segmentation and quantification, respectively. The proposed method allows for fast quantitative IRC volume measurements that can be used to improve patient care, reduce costs, and allow fast and reliable analysis in large population studies.

“…It is specified the type of learning followed (supervised if labeled samples were used to train the method and unsupervised if the methodology is capable of separate the classes without labeled examples), the type of algorithm (semiautomatic if it needs the intervention of the user to generate the result, automatic if no further input is needed), type of pathology the system was tested with (if specified by the authors) and the knowledge domain that the methodology analyzes to generate the final result (2D if only features from a scan are used at a time and 3D if features from multiple consecutive OCT scans are considered). As the reader can see, the state of the art is currently following a classical segmentation paradigm, obtaining satisfactory results (as shown, for example, in the benchmarking test by Girish et al [36]) even with recent deep learning approximations. Nonetheless, an accurate segmentation is not always attainable in the case of retinal fluid accumulations.…”

Section: Introductionmentioning

confidence: 99%

Intraretinal fluid identification via enhanced maps using optical coherence tomography images

Vidal

Moura

Novo

et al. 2018

Nowadays, among the main causes of blindness in developed countries are age-related macular degeneration (AMD) and the diabetic macular edema (DME). Both diseases present, as a common symptom, the appearance of cystoid fluid regions inside the retinal layers. Optical coherence tomography (OCT) image modality was one of the main medical imaging techniques for the early diagnosis and monitoring of AMD and DME via this intraretinal fluid detection and characterization. We present a novel methodology to identify these fluid accumulations by means of generating binary maps (offering a direct representation of these areas) and heat maps (containing the region confidence). To achieve this, a set of 312 intensity and texture-based features were studied. The most relevant features were selected using the sequential forward selection (SFS) strategy and tested with three archetypal classifiers: LDC, SVM and Parzen window. Finally, the most proficient classifier is used to create the proposed maps. All of the tested classifiers returned satisfactory results, the best classifier achieving a mean test accuracy higher than 94% in all of the experiments. The suitability of the maps was evaluated in a context of a screening issue with three different datasets obtained with two different devices, testing the capabilities of the system to work independently of the used OCT device. The experiments with the map creation were performed using 323 OCT images. Using only the binary maps, more than 91.33% of the images were correctly classified. With only the heat maps, the proposed methodology correctly separated 93.50% of the images.